By using our site, you acknowledge that you have read and understand our Cookie PolicyPrivacy Policyand our Terms of Service. Cross Validated is a question and answer site for people interested in statistics, machine learning, data analysis, data mining, and data visualization. It only takes a minute to sign up. When I input real and fake images to the discriminator, the returned value is always the same?

Is this a sort of overfitting of the discriminator? What else can be the problem? This is unfortunately one of the issues when training GANs. In other words, there's nothing encouraging diversity in G's predictions.

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The trick is to balance the alternation. In your link, refer to the section on "Improving sample diversity" where they discuss recent results on using minibatches to avoid collapse. Sign up to join this community. The best answers are voted up and rise to the top. Home Questions Tags Users Unanswered.

Ask Question. Asked 2 years, 8 months ago. Active 1 year, 5 months ago. Viewed 2k times. Carl 9, 4 4 gold badges 34 34 silver badges 87 87 bronze badges. Maximilian Maximilian 5 5 bronze badges. Active Oldest Votes. Tessaracter 5 5 bronze badges. Alex R. I guess It's the generator that copies while you mention it as D.

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Featured on Meta. Community and Moderator guidelines for escalating issues via new response…. Feedback on Q2 Community Roadmap. Linked 4. Related 6. Hot Network Questions. Question feed. Cross Validated works best with JavaScript enabled.We propose to incorporate adversarial dropout in generative multi-adversarial networks by omitting or dropping out the feedback of each discriminator with some probability at the end of each batch.

Our approach forces the generator not to constrain its output to satisfy a single discriminator, but, instead, to satisfy a dynamic ensemble of discriminators. We show that the proposed framework, named Dropout-GAN, leads to a more generalized generator, promoting variety in the generated samples and avoiding the mode collapse problem commonly experienced with generative adversarial networks GAN. We provide evidence that applying adversarial dropout promotes sample diversity on multiple datasets of varied sizes, mitigating mode collapse on several GAN approaches.

Generative adversarial networks [ 13 ]or GAN, is a framework that integrates adversarial training in the generative modeling process. While the generator tries to fool the discriminator by producing fake samples that look realistic, the discriminator tries to distinguish between real and fake samples better over time, making it harder to be fooled by the generator.

This leads to a poor generator that is only able to produce samples within a narrow scope of the data space, resulting in the generation of only similarly looking samples. Hence, at the end of training, the generator comes short regarding learning the full data distribution, and, instead, is only able to learn a small segment of it.

This is the main issue we try to tackle in this work. In practice, it simply consists of omitting or dropping out, the output of some randomly chosen neurons with a probability d or dropout rate.

The intuition behind this process is to ensure that neurons are not entirely dependent on a specific set of other neurons to produce their outputs. Instead, with dropout, each neuron relies on the population behavior of several other neurons, promoting generalization in the network. Hence, the overall network becomes more flexible and less prone to overfitting.

The main idea of this work consists of applying the same dropout principles to generative multi-adversarial networks. By applying dropout on the feedback of each discriminator, we force the generator to not rely on a specific discriminator or discriminator ensemble to learn how to produce realistic samples.

Thus, the generator guides its learning from the varied feedback given by a dynamic ensemble of discriminators that changes at every batch. In our use case, one can then see mode collapse as a consequence of overfitting to the feedback of a single discriminator, or even a static ensemble of discriminators.

Hence, by dynamically changing the adversarial ensemble at every batch, the generator is stimulated to induce variety in its output to increase the chances of fooling the different possible discriminators that may remain in the ensemble at the end. Our main contributions can be stated as follows:. G maps a latent space to the data space by receiving noise as input and applying transformations to it to generate unseen samples, while D maps a given sample to a probability p of it coming from the real data distribution.

In the ideal setting, given enough iterations, G would eventually start producing samples that look so realistic that D would not be able to distinguish between real and fake samples anymore. However, due to training instability, this equilibrium is hard to reach in practice.

The two models play the following minimax game:. On the other hand, p r x represents the real data distribution and D x represents the output of Di.

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In order to maximize Eq. By contrast, to minimize Eq. As a workaround, the authors propose to maximize log D G z instead, making it no longer a minimax game.

We propose to integrate adversarial feedback dropout in generative multi-adversarial networks, forcing G to appease and learn from a dynamic ensemble of discriminators.

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This ultimately encourages G to produce samples from a variety of modes, since it now needs to fool the different possible discriminators that may remain in the ensemble. Variations in the ensemble are achieved by dropping out the feedback of each D with a certain probability d at the end of every batch. This means that G will only consider the loss of the remaining discriminators in the ensemble while updating its parameters at each iteration.

gan discriminator overfitting

Otherwise, this information is discarded:. There is, however, the possibility of all discriminators being dropped out from the set, leaving G without any guidance on how to further update its parameters. Hence, taking into consideration this special case, our final value function, Fis set as follows:.

It is important to note that each discriminator trains independently, i.

tempoGAN: A Temporally Coherent, Volumetric GAN for Super-resolution Fluid Flow

This implies that even if dropped out, each D updates its parameters at the end of every batch. The detailed algorithm of the proposed solution can be found in Algorithm 1. In this section, we provide a detailed study of the effects of using a different number of discriminators together with different dropout rates.Go back to part one to read my reasoning.

A GAN is a game of faking and detecting the fakes. Photo: Wikipedia. A GAN consists of two neural networks competing to become the best. A common analogy is with an art expert the discriminator and an art forger the generator. The discriminator at work Photo: via Lebanon Daily Star. After substantial training, the expert will know all about the type of artwork in question. If we were to draw a conceptual drawing of the architecture, it would be something like this:.

Training of the discriminator expert and generator forger is optimal when the discriminator gets it right half of the time and generally has no clue to if the presented sample is a masterpiece or a fake.

The reason for going back and forth between the two networks during training is to prevent overfitting and unbalance between the discriminator and the generator. Achieving good quality with network setup and parameters is not an easy task.

The models can face a range of challenges and yield an output quite different to what one would expect:. Generated images of animals, demonstrating challenges with global structure and counting. Photo: Goodfellow et al In the next blogwe will see how you can run a GAN to generate handwritten digits. Stay tuned! Business Trends.

Simen Huuse. Posted on June 6, 3 minute read. Follow RSS feed Like. The models can face a range of challenges and yield an output quite different to what one would expect: Generated images of animals, demonstrating challenges with global structure and counting.

Alert Moderator. Assigned tags. Related Blog Posts.By using our site, you acknowledge that you have read and understand our Cookie PolicyPrivacy Policyand our Terms of Service. Data Science Stack Exchange is a question and answer site for Data science professionals, Machine Learning specialists, and those interested in learning more about the field. It only takes a minute to sign up. I have previously implemented a simple CNN to classify them with relative success with keras.

I am trying to create a GAN that returns a sequence that is emblematic of the sequences that have a '1' label. Thus far I have managed to get functioning code that creates 'fake' vectors that are similar looking to the original ones I was advised that assessing the performance on test data might be a good indicator of this.

Hence as a separate task to the normal procedure of the GAN training I also assess the generator's performance on test data in each epoch. See above in the upper-left panel I have plotted the loss profiles of the generator and discriminator, in the upper right I have plotted the accuracy achieved at each epoch when validating on the test data and the bottom three figures are exemplary sequences output at different stages of the training indicated by arrows.

Here the generator seems to be overfitting and generating all 0 sequences- which is undesirable. As an attempt to mitigate this I trained the discriminator to reject all 0 sequences before freezing its' weights and initiating the GAN:. I cannot find a good recommendation as to when to stop training.

The following source recommends:. But I am not sure how to assess when this is happening based on the test accuracy graph displayed above. When can I know based on test validation, done in each epoch, when to stop training the GAN? How do I know if my generator is overfitting to the and what can I do to mitigate this?

Sign up to join this community. The best answers are voted up and rise to the top. Home Questions Tags Users Unanswered. Asked 1 year ago. Active 1 year ago.

Generative Adversarial Networks (GAN)

Viewed times. I have attempted to re-implement typical analysis of 2D tensors i. See below another exemplary output from a different task : Here the generator seems to be overfitting and generating all 0 sequences- which is undesirable. Still after doing this the above shown problem persists. Overall my questions are: 1. The observation that GAN produces all 0s is not over-fitting, it is under-fitting.My last post about DCGANs was primarily focused on the idea of replacing fully connected layers with convolutions and implementing upsampling convolutions with Keras.

This article will further explain the architectural guidelines mentioned by Raford et al. In designing this architecture, the authors cite three sources of inspiration.

gan discriminator overfitting

With these advancements in mind, the authors searched for a stable DC-GAN architecture and landed on the following architectural guidelines:.

These architectural guidelines have been later expanded on in modern GAN literature. Further architectural guidelines are presented by Salimans et al. Many applications for GANs have been explored and much of the research is trying to achieve higher quality image synthesis. Many of the methods for achieving high quality image synthesis are really supervised learning techniques, because they require class labels for conditioning.

The main idea here is to use the features learned by the discriminator as a feature extractor for a classification model. Specifically, Radford et al. The SVM model uses a loss function that aims to maximize inter-class distance based on the margin between closest points in each class, and a high-dimensional hyperplane.

The SVM model is a great classifier, however, it is not a feature extractor and applying an SVM to images as they are would result in an extremely large number of local minima, essentially rendering the problem intractable.

Thus, the DC-GAN serves as a feature extractor that reduces the dimensionality of the images in a semantically-preserving way, such that an SVM can learn a discriminative model.

Re-reading the paper, the idea of GAN overfitting was one that I think is especially interesting. Overfitting in the context of supervised learning is very intuitive:. The picture above is a common illustration of what overfitting looks like on a regression task.

The overly parametric model adjusts itself such that it exactly matches the training data and has no error. This is a very interesting idea in the context of GANs.

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It seems that the generator would be the most successful if it discards any attempt at adding stochastic changes to data points and just mimics the training data exactly. Radford et al. Another interesting technique for exploring overfitting in GANs not used in this paper, is to do a nearest neighbor search using L1 or L2 distance, or maybe even VGG feature distanceto grab the images from the training dataset that are most similar to a given generated image.

Feature visualization in CNNs is achieved as follows. A generator network is trained via gradient descent to produce an image that results in maximum activation from a given feature.

It is interesting to think that these are the features the discriminator is using to tell if images are real or fake. Latent Space interpolation is one of the most interesting subjects of GAN research because it enables control over the generator. For example, GANs may eventually be used to design websites.

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You would like to be able to control characteristics of the design or interpolate between designs. One interesting detail to the latent space interpolation discussed in this paper that I had originally missed is that they do not use the Z vectors of individual points.

Thank you for reading this article! I have found this paper to be very useful in my research on GANs. Each time I have returned to this paper, I have gained an appreciation for the finer details of the paper. This paper is one of the foundational works on GANs and I highly recommend checking it out, especially if you are interested in image generation. Sign in. Connor Shorten Follow. Towards Data Science A Medium publication sharing concepts, ideas, and codes.By using our site, you acknowledge that you have read and understand our Cookie PolicyPrivacy Policyand our Terms of Service.

Artificial Intelligence Stack Exchange is a question and answer site for people interested in conceptual questions about life and challenges in a world where "cognitive" functions can be mimicked in purely digital environment. It only takes a minute to sign up. I'm training a DCGAN model on a x dataset of images and after an hour of training the generator started to generate on the same latent space noise as during training images that are identical to the dataset. For example, if my dataset is images of cars, I should expect to see unexisting designs of cars, right?

Am I understanding this wrong? I know this is a very general question but I was wondering if this is what should happen and if I should try on different latent space values and then see proper results and not just copies of my dataset? It might be that your dataset of images is to small.

gan discriminator overfitting

Your discriminative network might hardlearn these images at which point your generative network can only produce good images if it copies the same images of your dataset.

Sign up to join this community. The best answers are voted up and rise to the top. Home Questions Tags Users Unanswered. Am I overfitting my GAN model? Ask Question. Asked 1 month ago. Active 1 month ago. Viewed 32 times. JingleBells JingleBells 3 3 bronze badges. Active Oldest Votes. Lustwelpintje Lustwelpintje 1 1 silver badge 7 7 bronze badges. My dataset consists of only 45 images.

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What can I do? I'll look into it. Sign up or log in Sign up using Google. Sign up using Facebook. Sign up using Email and Password. Post as a guest Name. Email Required, but never shown. The Overflow Blog. Socializing with co-workers while social distancing. Featured on Meta. Community and Moderator guidelines for escalating issues via new response….GitHub is home to over 40 million developers working together to host and review code, manage projects, and build software together.

If nothing happens, download GitHub Desktop and try again. If nothing happens, download Xcode and try again. If nothing happens, download the GitHub extension for Visual Studio and try again. A simple, clean TensorFlow implementation of Generative Adversarial Networks with a focus on modeling illustrations. These images were generated by the model after being trained on a custom dataset of about 20, anime faces that were automatically cropped from illustrations using a face detector.

Deep Learning 28: (2) Generative Adversarial Network (GAN) : Loss Derivation from Scratch

It is theoretically possible for the generator network to memorize training set images rather than actually generalizing and learning to produce novel images of its own.

To check for this, I randomly generate images and display the "closest" images in the training set according to mean squared error. The top row is randomly generated images, the columns are the closest 5 images in the training set. It is clear that the generator does not merely learn to copy training set images, but rather generalizes and is able to produce its own unique images.

Deeper into DCGANs

Generative Adversarial Networks consist of two neural networks: a discriminator and a generator. The discriminator receives both real images from the training set and generated images produced by the generator. The discriminator outputs the probability that an image is real, so it is trained to output high values for the real images and low values for the generated ones.

The generator is trained to produce images that the discriminator thinks are real. Both the discriminator and generator are trainined simultaneously so that they compete against each other.

As a result of this, the generator learns to produce more and more realistic images as it trains. No strided convolutions. The generator uses bilinear upsampling to upscale a feature blob by a factor of 2, followed by a stride-1 convolution layer. The discriminator uses a stride-1 convolution followed by 2x2 max pooling.

Minibatch discrimination. More fully connected layers in both the generator and discriminator. A novel regularization term applied to the generator network. Normally, increasing the number of fully connected layers in the generator beyond one triggers one of the most common failure modes when training GANs: the generator "collapses" the z-space and produces only a very small number of unique examples.

In other words, very different z vectors will produce nearly the same generated image. To fix this, I add a small auxiliary z-predictor network that takes as input the output of the last fully connected layer in the generator, and predicts the value of z. In other words, it attempts to learn the inverse of whatever function the generator fully connected layers learn.

The z-predictor network and generator are trained together to predict the value of z. This forces the generator fully connected layers to only learn those transformations that preserve information about z.

The result is that the aformentioned collapse no longer occurs, and the generator is able to leverage the power of the additional fully connected layers. The custom dataset I used is too large to add to a Github repository; I am currently finding a suitable way to distribute it. Instructions for training the model will be in this readme after I make the dataset available. Skip to content. Dismiss Join GitHub today GitHub is home to over 40 million developers working together to host and review code, manage projects, and build software together.

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